Overview

Dataset statistics

Number of variables12
Number of observations1532
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory149.6 KiB
Average record size in memory100.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly overall correlated with recency_days and 4 other fieldsHigh correlation
recency_days is highly overall correlated with gross_revenue and 2 other fieldsHigh correlation
invoice_no is highly overall correlated with gross_revenue and 4 other fieldsHigh correlation
qtde_items is highly overall correlated with gross_revenue and 5 other fieldsHigh correlation
qtde_produto is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
frequency is highly overall correlated with gross_revenue and 2 other fieldsHigh correlation
avg_basket_size is highly overall correlated with qtde_itemsHigh correlation
avg_unique_basket_size is highly overall correlated with qtde_produto and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 38.61887027)Skewed
frequency is highly skewed (γ1 = 36.9526308)Skewed
qtde_returns is highly skewed (γ1 = 37.17310928)Skewed
avg_basket_size is highly skewed (γ1 = 34.54413707)Skewed
customer_id has unique valuesUnique
recency_days has 25 (1.6%) zerosZeros
frequency has 199 (13.0%) zerosZeros

Reproduction

Analysis started2023-06-22 12:30:45.560792
Analysis finished2023-06-22 12:31:44.757255
Duration59.2 seconds
Software versionydata-profiling vv4.1.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct1532
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15178.853
Minimum12352
Maximum18282
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.0 KiB
2023-06-22T09:31:45.129765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12352
5-th percentile12592.2
Q113716.5
median15123.5
Q316657
95-th percentile17950.45
Maximum18282
Range5930
Interquartile range (IQR)2940.5

Descriptive statistics

Standard deviation1716.7544
Coefficient of variation (CV)0.11310172
Kurtosis-1.1716387
Mean15178.853
Median Absolute Deviation (MAD)1487.5
Skewness0.093211119
Sum23254003
Variance2947245.7
MonotonicityNot monotonic
2023-06-22T09:31:45.570910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
0.1%
15249 1
 
0.1%
15521 1
 
0.1%
12456 1
 
0.1%
13588 1
 
0.1%
12888 1
 
0.1%
16253 1
 
0.1%
15906 1
 
0.1%
17719 1
 
0.1%
17874 1
 
0.1%
Other values (1522) 1522
99.3%
ValueCountFrequency (%)
12352 1
0.1%
12359 1
0.1%
12362 1
0.1%
12375 1
0.1%
12379 1
0.1%
12380 1
0.1%
12381 1
0.1%
12383 1
0.1%
12384 1
0.1%
12395 1
0.1%
ValueCountFrequency (%)
18282 1
0.1%
18277 1
0.1%
18276 1
0.1%
18274 1
0.1%
18272 1
0.1%
18270 1
0.1%
18269 1
0.1%
18263 1
0.1%
18260 1
0.1%
18257 1
0.1%

gross_revenue
Real number (ℝ)

Distinct1530
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4236.1385
Minimum6.2
Maximum280206.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2023-06-22T09:31:46.093010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile263.876
Q1731.31
median1604.37
Q33502.7525
95-th percentile11102.388
Maximum280206.02
Range280199.82
Interquartile range (IQR)2771.4425

Descriptive statistics

Standard deviation14620.496
Coefficient of variation (CV)3.4513735
Kurtosis184.30578
Mean4236.1385
Median Absolute Deviation (MAD)1052.26
Skewness12.178236
Sum6489764.2
Variance2.1375891 × 108
MonotonicityNot monotonic
2023-06-22T09:31:46.563439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1353.74 2
 
0.1%
63 2
 
0.1%
5391.21 1
 
0.1%
683.46 1
 
0.1%
2446.6 1
 
0.1%
3000.2 1
 
0.1%
3181.04 1
 
0.1%
2478.95 1
 
0.1%
354.12 1
 
0.1%
3683.86 1
 
0.1%
Other values (1520) 1520
99.2%
ValueCountFrequency (%)
6.2 1
0.1%
6.9 1
0.1%
13.3 1
0.1%
15 1
0.1%
52.2 1
0.1%
63 2
0.1%
71.4 1
0.1%
76.5 1
0.1%
85 1
0.1%
89.2 1
0.1%
ValueCountFrequency (%)
280206.02 1
0.1%
259657.3 1
0.1%
194550.79 1
0.1%
168472.5 1
0.1%
143825.06 1
0.1%
124914.53 1
0.1%
117379.63 1
0.1%
91062.38 1
0.1%
81024.84 1
0.1%
66653.56 1
0.1%

recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct239
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.994125
Minimum0
Maximum373
Zeros25
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2023-06-22T09:31:47.092047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median28
Q377
95-th percentile266.9
Maximum373
Range373
Interquartile range (IQR)67

Descriptive statistics

Standard deviation83.87148
Coefficient of variation (CV)1.3106122
Kurtosis2.8444643
Mean63.994125
Median Absolute Deviation (MAD)24
Skewness1.8783408
Sum98039
Variance7034.4252
MonotonicityNot monotonic
2023-06-22T09:31:47.611056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 63
 
4.1%
2 58
 
3.8%
8 50
 
3.3%
3 49
 
3.2%
4 47
 
3.1%
22 36
 
2.3%
17 35
 
2.3%
16 34
 
2.2%
7 34
 
2.2%
10 34
 
2.2%
Other values (229) 1092
71.3%
ValueCountFrequency (%)
0 25
 
1.6%
1 63
4.1%
2 58
3.8%
3 49
3.2%
4 47
3.1%
5 21
 
1.4%
7 34
2.2%
8 50
3.3%
9 30
2.0%
10 34
2.2%
ValueCountFrequency (%)
373 2
0.1%
372 4
0.3%
371 1
 
0.1%
368 1
 
0.1%
366 3
0.2%
365 3
0.2%
364 1
 
0.1%
360 1
 
0.1%
359 1
 
0.1%
358 3
0.2%

invoice_no
Real number (ℝ)

Distinct58
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5339426
Minimum1
Maximum209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2023-06-22T09:31:48.111429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q38
95-th percentile22
Maximum209
Range208
Interquartile range (IQR)6

Descriptive statistics

Standard deviation11.806718
Coefficient of variation (CV)1.5671367
Kurtosis116.24442
Mean7.5339426
Median Absolute Deviation (MAD)3
Skewness8.590718
Sum11542
Variance139.39859
MonotonicityNot monotonic
2023-06-22T09:31:48.582658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 208
13.6%
1 193
12.6%
3 188
12.3%
4 172
11.2%
5 128
8.4%
6 103
 
6.7%
7 89
 
5.8%
8 71
 
4.6%
9 53
 
3.5%
10 39
 
2.5%
Other values (48) 288
18.8%
ValueCountFrequency (%)
1 193
12.6%
2 208
13.6%
3 188
12.3%
4 172
11.2%
5 128
8.4%
6 103
6.7%
7 89
5.8%
8 71
 
4.6%
9 53
 
3.5%
10 39
 
2.5%
ValueCountFrequency (%)
209 1
0.1%
201 1
0.1%
124 1
0.1%
97 1
0.1%
93 1
0.1%
91 1
0.1%
86 1
0.1%
73 1
0.1%
63 1
0.1%
62 1
0.1%

qtde_items
Real number (ℝ)

Distinct1189
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2385.0777
Minimum1
Maximum196915
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2023-06-22T09:31:49.049115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile108
Q1383.75
median911
Q31966
95-th percentile6173.25
Maximum196915
Range196914
Interquartile range (IQR)1582.25

Descriptive statistics

Standard deviation8031.4735
Coefficient of variation (CV)3.3673845
Kurtosis255.02823
Mean2385.0777
Median Absolute Deviation (MAD)627
Skewness13.329693
Sum3653939
Variance64504567
MonotonicityNot monotonic
2023-06-22T09:31:49.581081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 5
 
0.3%
114 5
 
0.3%
416 5
 
0.3%
288 5
 
0.3%
544 4
 
0.3%
76 4
 
0.3%
371 4
 
0.3%
91 4
 
0.3%
140 4
 
0.3%
800 4
 
0.3%
Other values (1179) 1488
97.1%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
12 2
0.1%
16 1
0.1%
18 1
0.1%
23 1
0.1%
26 1
0.1%
28 2
0.1%
29 1
0.1%
ValueCountFrequency (%)
196915 1
0.1%
80997 1
0.1%
80265 1
0.1%
77374 1
0.1%
69993 1
0.1%
64549 1
0.1%
64124 1
0.1%
63312 1
0.1%
58343 1
0.1%
57885 1
0.1%

qtde_produto
Real number (ℝ)

Distinct305
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.57376
Minimum1
Maximum1787
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2023-06-22T09:31:50.097494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q129
median65
Q3117
95-th percentile277
Maximum1787
Range1786
Interquartile range (IQR)88

Descriptive statistics

Standard deviation119.46902
Coefficient of variation (CV)1.2632364
Kurtosis65.180771
Mean94.57376
Median Absolute Deviation (MAD)41
Skewness6.0623329
Sum144887
Variance14272.846
MonotonicityNot monotonic
2023-06-22T09:31:50.611596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 23
 
1.5%
24 21
 
1.4%
25 20
 
1.3%
11 20
 
1.3%
20 19
 
1.2%
30 18
 
1.2%
17 18
 
1.2%
34 17
 
1.1%
66 17
 
1.1%
19 17
 
1.1%
Other values (295) 1342
87.6%
ValueCountFrequency (%)
1 11
0.7%
2 4
 
0.3%
3 7
0.5%
4 10
0.7%
5 14
0.9%
6 11
0.7%
7 11
0.7%
8 16
1.0%
9 14
0.9%
10 13
0.8%
ValueCountFrequency (%)
1787 1
0.1%
1768 1
0.1%
1323 1
0.1%
1119 1
0.1%
884 1
0.1%
819 1
0.1%
718 1
0.1%
714 1
0.1%
700 1
0.1%
636 1
0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1531
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.05008
Minimum3.1
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2023-06-22T09:31:51.091516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile6.4187828
Q115.507449
median19.133254
Q326.727426
95-th percentile94.740328
Maximum56157.5
Range56154.4
Interquartile range (IQR)11.219978

Descriptive statistics

Standard deviation1440.5126
Coefficient of variation (CV)19.719521
Kurtosis1503.7584
Mean73.05008
Median Absolute Deviation (MAD)4.8695923
Skewness38.61887
Sum111912.72
Variance2075076.5
MonotonicityNot monotonic
2023-06-22T09:31:51.555717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.83333333 2
 
0.1%
21.46140351 1
 
0.1%
25.44832 1
 
0.1%
14.24683908 1
 
0.1%
70.824 1
 
0.1%
29.9500813 1
 
0.1%
10.45126667 1
 
0.1%
19.69357664 1
 
0.1%
14.85782609 1
 
0.1%
18.1272973 1
 
0.1%
Other values (1521) 1521
99.3%
ValueCountFrequency (%)
3.1 1
0.1%
3.140802469 1
0.1%
3.157113402 1
0.1%
3.45 1
0.1%
3.487294118 1
0.1%
3.734567219 1
0.1%
3.823875969 1
0.1%
3.831937173 1
0.1%
3.864423077 1
0.1%
3.875769231 1
0.1%
ValueCountFrequency (%)
56157.5 1
0.1%
4453.43 1
0.1%
2027.86 1
0.1%
952.9875 1
0.1%
931.5 1
0.1%
835.864 1
0.1%
643.8585714 1
0.1%
602.4531323 1
0.1%
577.3020475 1
0.1%
512.877561 1
0.1%

avg_recency_days
Real number (ℝ)

Distinct899
Distinct (%)58.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.395509
Minimum1
Maximum362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2023-06-22T09:31:52.032106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q119.444444
median35
Q357.541667
95-th percentile111
Maximum362
Range361
Interquartile range (IQR)38.097222

Descriptive statistics

Standard deviation39.700919
Coefficient of variation (CV)0.89425529
Kurtosis14.194427
Mean44.395509
Median Absolute Deviation (MAD)17.821429
Skewness2.9290172
Sum68013.921
Variance1576.163
MonotonicityNot monotonic
2023-06-22T09:31:52.451346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 20
 
1.3%
6 15
 
1.0%
7 14
 
0.9%
5 13
 
0.8%
1 13
 
0.8%
14 12
 
0.8%
8 12
 
0.8%
3 11
 
0.7%
21 10
 
0.7%
28 10
 
0.7%
Other values (889) 1402
91.5%
ValueCountFrequency (%)
1 13
0.8%
1.5 1
 
0.1%
2 10
0.7%
2.5 1
 
0.1%
2.565517241 1
 
0.1%
3 11
0.7%
3.271929825 1
 
0.1%
3.321428571 1
 
0.1%
3.5 2
 
0.1%
4 20
1.3%
ValueCountFrequency (%)
362 1
0.1%
351 1
0.1%
324 1
0.1%
309 1
0.1%
293 1
0.1%
292 1
0.1%
288 1
0.1%
285 1
0.1%
266 1
0.1%
260 1
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct883
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.065215058
Minimum0
Maximum34
Zeros199
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2023-06-22T09:31:52.955953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.014482282
median0.024169184
Q30.041124116
95-th percentile0.1
Maximum34
Range34
Interquartile range (IQR)0.026641834

Descriptive statistics

Standard deviation0.88606993
Coefficient of variation (CV)13.586892
Kurtosis1409.0501
Mean0.065215058
Median Absolute Deviation (MAD)0.012229437
Skewness36.952631
Sum99.909468
Variance0.78511991
MonotonicityNot monotonic
2023-06-22T09:31:53.435517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 199
 
13.0%
0.07142857143 9
 
0.6%
0.01923076923 8
 
0.5%
0.0303030303 7
 
0.5%
0.025 6
 
0.4%
0.01960784314 6
 
0.4%
0.02127659574 6
 
0.4%
0.02380952381 6
 
0.4%
0.02325581395 6
 
0.4%
0.02197802198 6
 
0.4%
Other values (873) 1273
83.1%
ValueCountFrequency (%)
0 199
13.0%
0.005494505495 1
 
0.1%
0.005698005698 1
 
0.1%
0.005730659026 1
 
0.1%
0.005847953216 1
 
0.1%
0.005917159763 1
 
0.1%
0.005970149254 2
 
0.1%
0.006024096386 1
 
0.1%
0.006472491909 1
 
0.1%
0.006535947712 1
 
0.1%
ValueCountFrequency (%)
34 1
0.1%
6 1
0.1%
2 2
0.1%
1.333333333 1
0.1%
1 2
0.1%
0.5603217158 1
0.1%
0.5403225806 1
0.1%
0.5 2
0.1%
0.3333333333 2
0.1%
0.2857142857 2
0.1%

qtde_returns
Real number (ℝ)

Distinct216
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.21932
Minimum1
Maximum80995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2023-06-22T09:31:53.911393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median9
Q326
95-th percentile234.45
Maximum80995
Range80994
Interquartile range (IQR)23

Descriptive statistics

Standard deviation2105.2918
Coefficient of variation (CV)17.085728
Kurtosis1425.157
Mean123.21932
Median Absolute Deviation (MAD)7
Skewness37.173109
Sum188772
Variance4432253.7
MonotonicityNot monotonic
2023-06-22T09:31:54.935357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 183
 
11.9%
2 153
 
10.0%
3 107
 
7.0%
4 90
 
5.9%
6 72
 
4.7%
5 64
 
4.2%
8 49
 
3.2%
12 48
 
3.1%
7 47
 
3.1%
9 38
 
2.5%
Other values (206) 681
44.5%
ValueCountFrequency (%)
1 183
11.9%
2 153
10.0%
3 107
7.0%
4 90
5.9%
5 64
 
4.2%
6 72
 
4.7%
7 47
 
3.1%
8 49
 
3.2%
9 38
 
2.5%
10 34
 
2.2%
ValueCountFrequency (%)
80995 1
0.1%
9014 1
0.1%
8060 1
0.1%
4627 1
0.1%
3768 1
0.1%
3335 1
0.1%
2975 1
0.1%
2022 1
0.1%
2012 1
0.1%
1920 1
0.1%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1275
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.14577
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2023-06-22T09:31:55.449038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile51.3875
Q1119.65179
median194.05357
Q3304.59539
95-th percentile684.665
Maximum40498.5
Range40497.5
Interquartile range (IQR)184.94361

Descriptive statistics

Standard deviation1073.3752
Coefficient of variation (CV)3.6867276
Kurtosis1288.712
Mean291.14577
Median Absolute Deviation (MAD)88.363095
Skewness34.544137
Sum446035.32
Variance1152134.2
MonotonicityNot monotonic
2023-06-22T09:31:55.923745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114 6
 
0.4%
288 5
 
0.3%
190 5
 
0.3%
86 5
 
0.3%
82 5
 
0.3%
208 4
 
0.3%
185.5 4
 
0.3%
80.5 4
 
0.3%
298 4
 
0.3%
88 4
 
0.3%
Other values (1265) 1486
97.0%
ValueCountFrequency (%)
1 1
0.1%
1.5 1
0.1%
2 1
0.1%
5.333333333 1
0.1%
6.142857143 1
0.1%
12 2
0.1%
13 1
0.1%
18 1
0.1%
19 1
0.1%
22 1
0.1%
ValueCountFrequency (%)
40498.5 1
0.1%
6009.333333 1
0.1%
3684.47619 1
0.1%
2697.465753 1
0.1%
2183.2 1
0.1%
2160.333333 1
0.1%
2141.5 1
0.1%
2082.225806 1
0.1%
1953.5 1
0.1%
1903.5 1
0.1%

avg_unique_basket_size
Real number (ℝ)

Distinct763
Distinct (%)49.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.114881
Minimum1
Maximum300.64706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2023-06-22T09:31:56.421995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.0611111
Q111
median17.5
Q326.6
95-th percentile55.534375
Maximum300.64706
Range299.64706
Interquartile range (IQR)15.6

Descriptive statistics

Standard deviation19.600326
Coefficient of variation (CV)0.88629579
Kurtosis36.330963
Mean22.114881
Median Absolute Deviation (MAD)7.4305556
Skewness4.158966
Sum33879.997
Variance384.17277
MonotonicityNot monotonic
2023-06-22T09:31:56.869089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 29
 
1.9%
14 24
 
1.6%
11 21
 
1.4%
20 19
 
1.2%
17 18
 
1.2%
5 17
 
1.1%
21 17
 
1.1%
9 16
 
1.0%
18 16
 
1.0%
10 16
 
1.0%
Other values (753) 1339
87.4%
ValueCountFrequency (%)
1 15
1.0%
1.25 1
 
0.1%
1.333333333 1
 
0.1%
1.5 2
 
0.1%
1.555555556 1
 
0.1%
1.571428571 1
 
0.1%
1.666666667 1
 
0.1%
1.9 1
 
0.1%
2 4
 
0.3%
2.222222222 1
 
0.1%
ValueCountFrequency (%)
300.6470588 1
0.1%
203.5 1
0.1%
149 1
0.1%
136.25 1
0.1%
135.75 1
0.1%
127 1
0.1%
122 1
0.1%
110.3333333 1
0.1%
110 1
0.1%
109.4545455 1
0.1%

Interactions

2023-06-22T09:31:38.773569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:46.283171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:51.340567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:55.800127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:00.540689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:04.853235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:09.700153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:14.207165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:18.532961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:23.677531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:28.461735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:33.535137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:39.168512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:46.680424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:51.712838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:56.334985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:00.889117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:05.226688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:10.087977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:14.548784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:18.901257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:24.082185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:28.887961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:33.932204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:39.544507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:47.654207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:52.028515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:56.716528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:01.215252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:05.580845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:10.443093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:14.907190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:19.293711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:24.516270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:29.311069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:34.326749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:39.946771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:48.034437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:52.363634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:57.112459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:01.542212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:05.965228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:10.821658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:15.294387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:20.004446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:24.915841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:29.712369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:34.760690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:40.326703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-06-22T09:31:15.623194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:20.379404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-06-22T09:31:30.137986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-06-22T09:31:40.783634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:48.762368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:53.065431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:57.813673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:02.214890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-06-22T09:31:20.776535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-06-22T09:31:30.593788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:35.559480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-06-22T09:30:49.143403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:53.436790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:58.161930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:02.579161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:07.297896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-06-22T09:31:02.912020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:07.666034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-06-22T09:31:16.687253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:21.550942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:26.490093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:31.434555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:36.379499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:41.890980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:49.840052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:54.202196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:58.949491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:03.302580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:08.066833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:12.719453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:17.010865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:21.993857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:26.915410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:31.887677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:37.155207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:42.287943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:50.221972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:54.580913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:59.347584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:03.735264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:08.421620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:13.058371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:17.388128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:22.408104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:27.291660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:32.318547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:37.546511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:42.753203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:50.589885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:54.970227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:59.786743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:04.111056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:08.833790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:13.457484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:17.787370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:22.831060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:27.667174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:32.755742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:37.963845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:43.177693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:50.958437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:30:55.410815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:00.191932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:04.504226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:09.289355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:13.835815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:18.184476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:23.281101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:28.062337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:33.186364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-22T09:31:38.379445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-06-22T09:31:57.292556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
customer_idgross_revenuerecency_daysinvoice_noqtde_itemsqtde_produtoavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
customer_id1.000-0.071-0.0180.034-0.050-0.021-0.1100.0330.007-0.061-0.116-0.066
gross_revenue-0.0711.000-0.5340.8260.9420.7290.227-0.1410.5510.3880.4850.264
recency_days-0.018-0.5341.000-0.614-0.525-0.4700.0140.050-0.385-0.187-0.090-0.077
invoice_no0.0340.826-0.6141.0000.7820.6590.057-0.1570.6680.3230.0640.004
qtde_items-0.0500.942-0.5250.7821.0000.7230.153-0.1340.5200.3970.6280.293
qtde_produto-0.0210.729-0.4700.6590.7231.000-0.371-0.0310.4200.2270.3520.689
avg_ticket-0.1100.2270.0140.0570.153-0.3711.000-0.0790.0570.1870.179-0.548
avg_recency_days0.033-0.1410.050-0.157-0.134-0.031-0.0791.000-0.442-0.165-0.0330.057
frequency0.0070.551-0.3850.6680.5200.4200.057-0.4421.0000.2590.024-0.040
qtde_returns-0.0610.388-0.1870.3230.3970.2270.187-0.1650.2591.0000.228-0.009
avg_basket_size-0.1160.485-0.0900.0640.6280.3520.179-0.0330.0240.2281.0000.485
avg_unique_basket_size-0.0660.264-0.0770.0040.2930.689-0.5480.057-0.040-0.0090.4851.000

Missing values

2023-06-22T09:31:43.731139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-22T09:31:44.413352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysinvoice_noqtde_itemsqtde_produtoavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
0178505391.21372.034.01733.021.018.15222235.50000034.00000040.050.9705888.735294
1130473237.5431.010.01391.0106.018.82290726.3076920.02924036.0139.10000017.200000
2125837281.382.015.05060.0115.029.47927121.8235290.04043151.0337.33333316.466667
415100876.00333.03.080.01.0292.0000008.6000000.07500022.026.6666671.000000
5152914668.3025.015.02103.062.045.32330121.7500000.04310329.0140.2000006.866667
6146885630.877.021.03621.0148.017.21978618.3000000.057377399.0172.42857115.571429
7178095411.9116.012.02057.046.088.71983632.4545450.03361342.0171.4166675.083333
81531160767.900.091.038194.0567.025.5434644.1444440.243968474.0419.71428626.142857
9145278508.822.055.02089.0330.08.7539305.8888890.14986440.037.98181817.672727
131602981024.8438.063.040208.044.0334.8133887.6136360.1880608060.0638.2222223.841270
customer_idgross_revenuerecency_daysinvoice_noqtde_itemsqtde_produtoavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
57441383252.2019.01.028.03.017.4000002.00.0000003.028.0000003.0
5758181398438.3417.06.05557.035.053.0713211.06.00000096.0926.16666726.5
576017010195.3618.01.0118.011.017.7600002.00.0000002.0118.00000011.0
577016956308.7417.01.0114.016.019.2962508.00.0000002.0114.00000016.0
577315877493.281.02.0371.0122.03.82387616.00.125000125.0185.50000064.5
578516376987.018.02.0694.0104.07.8960809.00.2222225.0347.00000062.5
5808177271060.2515.01.0645.066.016.0643946.00.0000006.0645.00000066.0
584012479527.2011.01.0385.031.017.0064524.00.00000034.0385.00000031.0
586114126706.137.03.0508.014.047.0753333.01.00000050.0169.3333335.0
589612558269.967.01.0196.011.024.5418186.00.000000196.0196.00000011.0